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Shilpi Bhattacharyya

Masters/Computer Science
Data Scientist, Data & AI Expert


How Did You Find Your Job?
I applied for the position on IBM’s career website and following that I had a series of interviews before I got an offer.

Position Description

At IBM, Data scientists work with enterprise leaders and key decision makers to solve business problems by preparing, analyzing, and understanding data to deliver insight, predict emerging trends, and provide recommendations to optimize results. Data scientists use a variety of data (structured, unstructured, IoT streaming), analytics, AI tools, and programming languages often using a cloud infrastructure to handle the volume and veracity of data streams. Modern applications of data science range from traditional transactional data analytics to natural language processing and computer vision, with a variety of analytical tools, machine learning and AI algorithms. Armed with data, modeling expertise, and analytic results, the data scientist communicates conclusions and recommendations to stakeholders in an organization's leadership structure. Business acumen is an important skill for data scientists, for example, in understanding the problem, formulating hypotheses and testing conclusions to determine appropriate methods to influence strategic choices through data. To effectively communicate their findings to business leaders, data scientists need strong consulting, communication, visualization, and storytelling skills. 

Structure of a Typical Day

I work in the Cloud garage at IBM which has a start-up culture of building fast and we are very agile in the way we work. I do not have a typical day as I travel frequently to client offices in different time zones. I help clients on a 6-8-week basis as an expert to understand their pain points with regards to discovering efficient use of data using machine learning techniques (sometimes out of the box Watson products) by building a prototype/MVP (minimum viable product) for them which could solve their business problems. We work in a collaborative way to engage the corresponding data scientists from the client’s organization so that once we develop the prototype, they can carry our methodology forward and continue building on the MVP delivered to achieve future success..

Advice to Other Students

There is no substitute to hard work. Be passionate about what you do and always plan for the long run.